candy_file <- "candy-data.csv"

candy = read.csv(candy_file, row.names=1)
head(candy)

Q1. How many different candy types are in this dataset?

nrow(candy)
## [1] 85

There are 85 types of candy.

Q2. How many fruity candy types are in the dataset?

sum(candy[,2])
## [1] 38

There are 38 fruity candy types.

candy["Twix", ]$winpercent
## [1] 81.64291

Q3. What is your favorite candy in the dataset and what is it’s winpercent value?

candy["Sour Patch Kids", ]$winpercent
## [1] 59.864

Q4. What is the winpercent value for “Kit Kat”?

candy["Kit Kat", ]$winpercent
## [1] 76.7686

Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?

candy["Tootsie Roll Snack Bars", ]$winpercent
## [1] 49.6535
library("skimr")
## Warning: package 'skimr' was built under R version 4.3.3
skim(candy)
Data summary
Name candy
Number of rows 85
Number of columns 12
_______________________
Column type frequency:
numeric 12
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
chocolate 0 1 0.44 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
fruity 0 1 0.45 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
caramel 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
peanutyalmondy 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
nougat 0 1 0.08 0.28 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
crispedricewafer 0 1 0.08 0.28 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
hard 0 1 0.18 0.38 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
bar 0 1 0.25 0.43 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
pluribus 0 1 0.52 0.50 0.00 0.00 1.00 1.00 1.00 ▇▁▁▁▇
sugarpercent 0 1 0.48 0.28 0.01 0.22 0.47 0.73 0.99 ▇▇▇▇▆
pricepercent 0 1 0.47 0.29 0.01 0.26 0.47 0.65 0.98 ▇▇▇▇▆
winpercent 0 1 50.32 14.71 22.45 39.14 47.83 59.86 84.18 ▃▇▆▅▂

Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?

All percentages are continuous between 0 and 1 except winpercent looks to be on a different scale. It seems to be in % but not in decimal. All types column are either 0 or 1.

Q7. What do you think a zero and one represent for the candy$chocolate column?

A zero means this candy type does not contain chocolate and a one means it contains chocolate.

Q8. Plot a histogram of winpercent values

hist(candy$winpercent)

Q9. Is the distribution of winpercent values symmetrical?

No, it is not symmetrical.

Q10. Is the center of the distribution above or below 50%?

It is below 50%

Q11. On average is chocolate candy higher or lower ranked than fruit candy?

chocolate<-candy$winpercent[as.logical(candy$chocolate)]
fruity<-candy$winpercent[as.logical(candy$fruity)]
t.test(chocolate,fruity)
## 
##  Welch Two Sample t-test
## 
## data:  chocolate and fruity
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  11.44563 22.15795
## sample estimates:
## mean of x mean of y 
##  60.92153  44.11974

chocolate candy is ranked higher.

Q12. Is this difference statistically significant?

Yes. Because the t value is 2.87 e-08 which is very very small.

Q13. What are the five least liked candy types in this set?

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
head(candy[order(candy$winpercent),], n=5)
candy %>% arrange(winpercent) %>% head(5)

Q14. What are the top 5 all time favorite candy types out of this set?

candy %>% arrange(desc(winpercent)) %>% head(5)

Q15. Make a first barplot of candy ranking based on winpercent values.

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
ggplot(candy) + 
  aes(winpercent, rownames(candy)) +
  geom_col()

Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?

ggplot(candy) + 
  aes(winpercent, reorder(rownames(candy),winpercent)) +  
  geom_col()

my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"
ggplot(candy) + 
  aes(winpercent, reorder(rownames(candy),winpercent)) +
  geom_col(fill=my_cols)

Now, for the first time, using this plot we can answer questions like: - Q17. What is the worst ranked chocolate candy?

Sixlets

library(ggrepel)

# How about a plot of price vs win
ggplot(candy) +
  aes(winpercent, pricepercent, label=rownames(candy)) +
  geom_point(col=my_cols) + 
  geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)
## Warning: ggrepel: 53 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?

Reese’s Miniatures.

Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?

ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )

Nik L Nip is lease popular among the 5.

# Make a lollipop chart of pricepercent
ggplot(candy) +
  aes(pricepercent, reorder(rownames(candy), pricepercent)) +
  geom_segment(aes(yend = reorder(rownames(candy), pricepercent), 
                   xend = 0), col="gray40") +
    geom_point()

library(corrplot)
## Warning: package 'corrplot' was built under R version 4.3.3
## corrplot 0.92 loaded
cij <- cor(candy)
corrplot(cij)

Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)? Fruity and chocolate,fruity and bar, pluribus and bar.

Q23. Similarly, what two variables are most positively correlated?

chocolate and winpercent, chocolate and bar.

pca <- prcomp(candy, scale=TRUE)
summary(pca)
## Importance of components:
##                           PC1    PC2    PC3     PC4    PC5     PC6     PC7
## Standard deviation     2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion  0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
##                            PC8     PC9    PC10    PC11    PC12
## Standard deviation     0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion  0.89998 0.93832 0.97071 0.98683 1.00000
plot(pca$x[, 1:2])

plot(pca$x[,1:2], col=my_cols, pch=16)

# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) + 
        aes(x=PC1, y=PC2, 
            size=winpercent/100,  
            text=rownames(my_data),
            label=rownames(my_data)) +
        geom_point(col=my_cols)

p

library(ggrepel)

p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7)  + 
  theme(legend.position = "none") +
  labs(title="Halloween Candy PCA Space",
       subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
       caption="Data from 538")
## Warning: ggrepel: 40 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(p)
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")

Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you? HINT. pluribus means the candy comes in a bag or box of multiple candies.

Fruity and pluribus. Yes since fruity candys usually comes in a bag or box of multiple candies.